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Intelligent prediction of heat capacity of polyethylene glycol polymer

Abstract Polyethylene glycol (PEG) exhibits tunable molar heat capacity, making it essential for thermal management and materials processing. Using an experimental databank of 528 observations, this study applies machine learning to develop predictive models based on temperature and molar mass. Data...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Jamal I. Al-Nabulsi, Zaid Ajzan Alsalami, J. Deepak, Johar MGM, Anupama Routray, A. Karthikeyan, Harjot Singh Gill, Yashwant Singh Bisht, Ahmad Abumalek
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2025-11-01
Schriftenreihe:Scientific Reports
Schlagworte:
Machine learning
Polyethylene glycol polymer
Heat capacity prediction
Monte carlo outlier detection
SHAP analysis
Online-Zugang:https://doi.org/10.1038/s41598-025-25326-4
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